where \(\theta \mapsto L_n(\theta ) = L(\theta ; D^n)\)  is the likelihood function and \(\Pi \)  is a prior distribution on \(\Theta \) . Among the first papers to adopt such an approach is [1]}, followed up on by [2]}. [3]} showed that (REF ) is the principled way to update prior beliefs when the model is potentially misspecified, and that the appearance of a non-trivial learning rate is a necessary by-product. A different connection between robustness and learning rate \(\eta < 1\)  was made recently in [4]}. More details about model misspecification and generalized Bayes are given in Section .
